Abstract: Based on data of occurrence degree of rice planthoppers from 45 agricultural pest monitoring stations in Guangxi Province during 1988 to 2012 as well as data of meteorological factors and atmospheric circulation characteristics during 1987 to 2012, three zones with different occurrence degrees of early rice planthoppers were divided: east Guangxi, southwest Guangxi, and northwest Guangxi. Occurrence degree of early rice planthoppers was predicted in each zone by fuzzy cluster analysis, and BP neural network. The results showed that the occurrence degree of rice planthoppers was closely correlated with meteorological factors and atmospheric general circulation in Guangxi. High temperature, frequent rainy days, high humidity and insufficient sunshine in winter and spring seasons were beneficial to the occurrence of rice planthoppers, and subtropical high, IndiaBurma trough and southwest airflow also affected the occurrence degree of rice planthoppers. Original predictive factors for the occurrence degree of early rice planthoppers in each zone were selected from the meteorological factors in winter and spring seasons and the atmospheric circulation characteristics to build comprehensive predictors using EOF decomposition method, and then prediction models for the occurrence degree of rice planthoppers were established in each zone. The crosstest showed that the average absolute fitting error was lower in BP neural network model than in step regression by 0.07 in east Guangxi, 0.1 in southwest Guangxi, and 0.02 in northwest Guangxi. The prediction using independentsamples in 2011 to 2012 showed that the mean predicted absolute errors were 0.42 for BP neural network model and 0.5 for step regression, indicating that the nonlinear correlation between rice planthoppers and meteorological factors is better predicted by BP neural network model.

Key words: grey relational analysis, chilling damage, rice, northeast China, comprehensive assessment.